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International Conference on Intelligent Autonomous Systems

IAS 2018: Intelligent Autonomous Systems 15 pp 186–198Cite as

Convolutional Channel Features-Based Person Identification for Person Following Robots

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 867))

Abstract

This paper describes a novel person identification framework for mobile robots. In this framework, we combine Convolutional Channel Features (CCF) and online boosting to construct a classifier of a target person to be followed. It allows us to take advantage of deep neural network-based feature representation and adapt the person classifier to the specific target person depending on circumstances. Through evaluations, we validated that the proposed method outperforms existing person identification methods for mobile robots. We applied the proposed method to a real person following robot, and it has been shown that CCF-based person identification realizes robust person following.

Video available at: https://www.youtube.com/watch?v=semX5Li0yxQ.

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Notes

  1. 1.

    https://github.com/koide3/ccf_feature_extraction.

References

  1. Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3908–3916. IEEE (2015)

    Google Scholar 

  2. Alvarez-Santos, V., Pardo, X.M., Iglesias, R., Canedo-Rodriguez, A., Regueiro, C.V.: Feature analysis for human recognition and discrimination: application to a person-following behaviour in a mobile robot. Robot. Auton. Syst. 60(8), 1021–1036 (2012)

    Article  Google Scholar 

  3. Arras, K.O., Mozos, O.M., Burgard, W.: Using boosted features for the detection of people in 2D range data. In: IEEE International Conference on Robotics and Automation, pp. 3402–3407. IEEE (2007)

    Google Scholar 

  4. Berdugo, G., Soceanu, O., Moshe, Y., Rudoy, D., Dvir, I.: Object reidentification in real world scenarios across multiple non-overlapping cameras. In: European Signal Processing Conference, pp. 1806–1810 (2010)

    Google Scholar 

  5. Chen, B.X., Sahdev, R., Tsotsos, J.K.: Integrating stereo vision with a CNN tracker for a person-following robot. In: Lecture Notes in Computer Science, pp. 300–313. Springer (2017)

    Google Scholar 

  6. Chen, B.X., Sahdev, R., Tsotsos, J.K.: Person following robot using selected online ada-boosting with stereo camera. In: Conference on Computer and Robot Vision, pp. 48–55 (2017)

    Google Scholar 

  7. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. IEEE (2005)

    Google Scholar 

  8. Grabner, H., Bischof, H.: On-line boosting and vision. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 260–267. IEEE (2006)

    Google Scholar 

  9. Koide, K., Miura, J.: Identification of a specific person using color, height, and gait features for a person following robot. Robot. Auton. Syst. 84, 76–87 (2016)

    Article  Google Scholar 

  10. Leigh, A., Pineau, J., Olmedo, N., Zhang, H.: Person tracking and following with 2D laser scanners. In: IEEE International Conference on Robotics and Automation, pp. 726–733 (2015)

    Google Scholar 

  11. Li, W., Zhao, R., Wang, X.: Human reidentification with transferred metric learning. In: Asian Conference on Computer Vision (2012)

    Google Scholar 

  12. Li, W., Zhao, R., Xiao, T., Wang, X.: DeepReID: deep filter pairing neural network for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition (2014)

    Google Scholar 

  13. Luber, M., Spinello, L., Arras, K.O.: People tracking in RGB-d data with on-line boosted target models. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3844–3849. IEEE (2011)

    Google Scholar 

  14. Makihara, Y., Mannami, H., Yagi, Y.: Gait analysis of gender and age using a large-scale multi-view gait database. In: Asian Conference on Computer Vision, pp. 440–451. Springer (2011)

    Google Scholar 

  15. Munaro, M., Ghidoni, S., Dizmen, D.T., Menegatti, E.: A feature-based approach to people re-identification using skeleton keypoints. In: In: IEEE International Conference on Robotics and Automation, pp. 5644–5651. IEEE (2014)

    Google Scholar 

  16. Munaro, M., Menegatti, E.: Fast RGB-d people tracking for service robots. Auton. Robot. 37(3), 227–242 (2014)

    Article  Google Scholar 

  17. Radosavljevic, Z.: A study of a target tracking method using global nearest neighbor algorithm. Vojnotehnicki glasnik 2, 160–167 (2006)

    Article  Google Scholar 

  18. Sahoo, D., Pham, Q., Lu, J., Hoi, S.C.H.: Online deep learning: Learning deep neural networks on the fly. CoRR abs/1711.03705 (2017). arXiv:1711.03705

  19. Satake, J., Chiba, M., Miura, J.: A SIFT-based person identification using a distance-dependent appearance model for a person following robot. In: IEEE International Conference on Robotics and Biomimetics, pp. 962–967. IEEE (2012)

    Google Scholar 

  20. Schumann, A., Stiefelhagen, R.: Person re-identification by deep learning attribute-complementary information. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops. IEEE (2017)

    Google Scholar 

  21. Yang, B., Yan, J., Lei, Z., Li, S.Z.: Convolutional channel features. In: IEEE International Conference on Computer Vision. IEEE (2015)

    Google Scholar 

  22. Zainudin, Z., Kodagoda, S., Dissanayake, G.: Torso detection and tracking using a 2D laser range finder. In: Australasian Conference on Robotics and Automation, ARAA (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Toyohashi University of Technology, Toyohashi, Aichi, Japan

    Kenji Koide & Jun Miura

Authors
  1. Kenji Koide
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  2. Jun Miura
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Corresponding author

Correspondence to Kenji Koide .

Editor information

Editors and Affiliations

  1. Baden-Wuerttemberg Cooperative State University, Karlsruhe, Germany

    Marcus Strand

  2. Humanoids and Intelligence Systems Lab, KIT - Karlsruher Institut für Technologie, Karlsruhe, Germany

    Rüdiger Dillmann

  3. University of Padua , Padua, Italy

    Emanuele Menegatti

  4. University of Padua, Padua, Italy

    Stefano Ghidoni

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Koide, K., Miura, J. (2019). Convolutional Channel Features-Based Person Identification for Person Following Robots. In: Strand, M., Dillmann, R., Menegatti, E., Ghidoni, S. (eds) Intelligent Autonomous Systems 15. IAS 2018. Advances in Intelligent Systems and Computing, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-01370-7_15

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